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A boosted chimp optimizer for numerical and engineering design optimization challenges

Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp’s amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minim...

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Autores principales: Kumari, Ch. Leela, Kamboj, Vikram Kumar, Bath, S. K., Tripathi, Suman Lata, Khatri, Megha, Sehgal, Shivani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8945882/
https://www.ncbi.nlm.nih.gov/pubmed/35350647
http://dx.doi.org/10.1007/s00366-021-01591-5
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author Kumari, Ch. Leela
Kamboj, Vikram Kumar
Bath, S. K.
Tripathi, Suman Lata
Khatri, Megha
Sehgal, Shivani
author_facet Kumari, Ch. Leela
Kamboj, Vikram Kumar
Bath, S. K.
Tripathi, Suman Lata
Khatri, Megha
Sehgal, Shivani
author_sort Kumari, Ch. Leela
collection PubMed
description Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp’s amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minima for high-dimensional problems. This research focuses on the improved variants of the chimp optimizer algorithm and named as Boosted chimp optimizer algorithms. In one of the proposed variants, the existing chimp optimizer algorithm has been combined with SHO algorithm to improve the exploration phase of the existing chimp optimizer and named as IChoA-SHO and other variant is proposed to improve the exploitation search capability of the existing ChoA. The testing and validation of the proposed optimizer has been done for various standard benchmarks and Non-convex, Non-linear, and typical engineering design problems. The proposed variants have been evaluated for seven standard uni-modal benchmark functions, six standard multi-modal benchmark functions, ten standard fixed-dimension benchmark functions, and 11 types of multidisciplinary engineering design problems. The outcomes of this method have been compared with other existing optimization methods considering convergence speed as well as for searching local and global optimal solutions. The testing results show the better performance of the proposed methods excel than the other existing optimization methods.
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spelling pubmed-89458822022-03-25 A boosted chimp optimizer for numerical and engineering design optimization challenges Kumari, Ch. Leela Kamboj, Vikram Kumar Bath, S. K. Tripathi, Suman Lata Khatri, Megha Sehgal, Shivani Eng Comput Original Article Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp’s amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minima for high-dimensional problems. This research focuses on the improved variants of the chimp optimizer algorithm and named as Boosted chimp optimizer algorithms. In one of the proposed variants, the existing chimp optimizer algorithm has been combined with SHO algorithm to improve the exploration phase of the existing chimp optimizer and named as IChoA-SHO and other variant is proposed to improve the exploitation search capability of the existing ChoA. The testing and validation of the proposed optimizer has been done for various standard benchmarks and Non-convex, Non-linear, and typical engineering design problems. The proposed variants have been evaluated for seven standard uni-modal benchmark functions, six standard multi-modal benchmark functions, ten standard fixed-dimension benchmark functions, and 11 types of multidisciplinary engineering design problems. The outcomes of this method have been compared with other existing optimization methods considering convergence speed as well as for searching local and global optimal solutions. The testing results show the better performance of the proposed methods excel than the other existing optimization methods. Springer London 2022-03-24 /pmc/articles/PMC8945882/ /pubmed/35350647 http://dx.doi.org/10.1007/s00366-021-01591-5 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Kumari, Ch. Leela
Kamboj, Vikram Kumar
Bath, S. K.
Tripathi, Suman Lata
Khatri, Megha
Sehgal, Shivani
A boosted chimp optimizer for numerical and engineering design optimization challenges
title A boosted chimp optimizer for numerical and engineering design optimization challenges
title_full A boosted chimp optimizer for numerical and engineering design optimization challenges
title_fullStr A boosted chimp optimizer for numerical and engineering design optimization challenges
title_full_unstemmed A boosted chimp optimizer for numerical and engineering design optimization challenges
title_short A boosted chimp optimizer for numerical and engineering design optimization challenges
title_sort boosted chimp optimizer for numerical and engineering design optimization challenges
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8945882/
https://www.ncbi.nlm.nih.gov/pubmed/35350647
http://dx.doi.org/10.1007/s00366-021-01591-5
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